Neural net for use in drilling simulation

ABSTRACT

A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application, pursuant to 35 U.S.C. §119(e), claims priority to U.S.Provisional Application Ser. No. 60/912,820, filed Apr. 19, 2007. Thatapplication is incorporated by reference in its entirety.

BACKGROUND

1. Field of the Disclosure

Embodiments disclosed herein are related generally to the field of welldrilling. More specifically, embodiments disclosed herein relate tomethods of optimizing drilling tool assemblies for use in well drillingoperations. More specifically still, embodiments disclosed herein relateto methods of optimizing drilling tool assembles using artificial neuralnetworks.

2. Background Art

FIG. 1 shows one example of a conventional drilling system for drillingan earth formation. The drilling system includes a drilling rig 10 usedto turn a drilling tool assembly 12 which extends downward into awellbore 14. Drilling tool assembly 12 includes a drilling string 16, abottom hole assembly (“BHA”) 18, and a drill bit 20, attached to thedistal end of drill string 16.

Drill string 16 comprises several joints of drill pipe 16 a connectedend to end through tool joints 16 b. Drill string 16 transmits drillingfluid (through its central bore) and transmits rotational power fromdrill rig 10 to BHA 18. In some cases drill string 16 Further includesadditional components such as subs, pup joints, etc. Drill pipe 16 aprovides a hydraulic passage through which drilling fluid is pumped. Thedrilling fluid discharges through selected-size orifices in the bit(“jets”) for the purposes of cooling the drill bit and lifting rockcuttings out of the wellbore as it is being drilled.

Bottom hole assembly 18 includes a drill bit 20. Typical BHAs may alsoinclude additional components attached between drill string 16 and drillbit 20. Examples of additional BHA components include drill collars,stabilizers, measurement-while-drilling (“MWD”) tools,logging-while-drilling (“LWD”) tools, and downhole motors.

In general, drilling tool assemblies 12 may include other drillingcomponents and accessories, such as special valves, kelly cocks, blowoutpreventers, and safety valves. Additional components included indrilling tool assemblies 12 may be considered a part of drill string 16or a part of BHA 18 depending on their locations in drilling toolassembly 12.

Drill bit 20 in BHA 18 may be any type of drill bit suitable fordrilling earth formation. The most common types of earth boring bitsused for drilling earth formations are fixed-cutter (or fixed-head)bits, roller cone bits, and percussion bits. FIG. 2 shows one example ofa fixed-cutter bit. FIG. 3 shows one example of a roller cone bit.

Referring now to FIG. 2, fixed-cutter bits (also called drag bits) 21typically comprise a bit body 22 having a threaded connection at one end24 and a cutting head 26 formed at the other end. Cutting head 26 offixed-cutter bit 21 typically comprises a plurality of ribs or blades 28arranged about a rotational axis of the bit and extending radiallyoutward from bit body 22. Cutting elements 29 are preferably embedded inthe blades 28 to engage formation as bit 21 is rotated on a bottomsurface of a wellbore. Cutting elements 29 of fixed-cutter bits maycomprise polycrystalline diamond compacts (“PDC”), speciallymanufactured diamond cutters, or any other cutter elements known tothose of ordinary skill in the art. These bits 21 are generally referredto as PDC bits.

Referring now to FIG. 3, a roller cone bit 30 typically comprises a bitbody 32 having a threaded connection at one end 34 and one or more legs31 extending from the other end. A roller cone 36 is mounted on ajournal (not shown) on each leg 31 and is able to rotate with respect tobit body 32. On each cone 36, a plurality of cutting elements 38 areshown arranged in rows upon the surface of cone 36 to contact and cut aformation encountered by bit 30. Roller cone bit 30 is designed suchthat as it rotates, cones 36 of bit 30 roll on the bottom surface of thewellbore and cutting elements 38 engage the formation therebelow. Insome cases, cutting elements 38 comprise milled steel teeth and in othercases, cutting elements 38 comprise hard metal inserts embedded in thecones. Typically, these inserts are tungsten carbide inserts orpolycrystalline diamond compacts, but in some cases, hardfacing isapplied to the surface of the cutting elements to improve wearresistance of the cutting structure.

Referring again to FIG. 1, for drill bit 20 to drill through formation,sufficient rotational moment and axial force must be applied to bit 20to cause the cutting elements to cut into and/or crush formation as bit20 is rotated. Axial force applied to bit 20 is typically referred to asthe weight on bit (“WOB”). Rotational moment applied to drilling toolassembly 12 by drill rig 10 (usually by a rotary table or a top drive)to turn drilling tool assembly 12 is referred to as the rotary torque.The speed at which drilling rig 10 rotates drilling tool assembly 12,typically measured in revolutions per minute (“RPM”), is referred to asthe rotary speed. Additionally, the portion of the weight of drillingtool assembly 12 supported by a suspending mechanism of rig 10 istypically referred to as the hook load.

The speed and economy with which a wellbore is drilled, as well as thequality of the hole drilled, depend on a number of factors. Thesefactors include, among others, the mechanical properties of the rockswhich are drilled, the diameter and type of the drill bit used, the flowrate of the drilling fluid, and the rotary speed and axial force appliedto the drill bit. It is generally the case that for any particularmechanical property of a formation, a drill bit's rate of penetration(“ROP”) corresponds to the amount of axial force on and the rotary speedof the drill bit. The rate at which the drill bit wears out is generallyrelated to the ROP. Various methods have been developed to optimizevarious drilling parameters to achieve various desirable results.

Prior art methods for optimizing values for drilling parameters thatprimarily involve looking at the formation have focused on thecompressive strength of the rock being drilled. For example, U.S. Pat.No. 6,346,595, issued to Civolani, el al. (“the '595 patent”), andassigned to the assignee of the present invention, discloses a method ofselecting a drill bit design parameter based on the compressive strengthof the formation. The compressive strength of the formation may bedirectly measured by an indentation test performed on drill cuttings inthe drilling fluid returns. The method may also be applied to determinethe likely optimum drilling parameters such as hydraulic requirements,gauge protection, WOB, and the bit rotation rate. The '595 patent ishereby incorporated by reference in its entirety.

U.S. Pat. No. 6,424,919, issued to Moran, et al. (“the '919 patent”),and assigned to the assignee of the present invention, discloses amethod of selecting a drill bit design parameter by inputting at leastone property of a formation to be drilled into a trained ArtificialNeural Network (“ANN”). The '919 patent also discloses that a trainedANN may be used to determine optimum drilling operating parameters for aselected drill bit design in a formation having particular properties.The ANN may be trained using data obtained from laboratoryexperimentation or from existing wells that have been drilled near thepresent well, such as an offset well. The '919 patent is herebyincorporated by reference in its entirety.

ANNs are a relatively new data processing mechanism. ANNs emulate theneuron interconnection architecture of the human brain to mimic theprocess of human thought. By using empirical pattern recognition, ANNshave been applied in many areas to provide sophisticated data processingsolutions to complex and dynamic problems (e.g., classification,diagnosis, decision making, prediction, voice recognition, militarytarget identification).

Similar to the human brain's problem solving process, ANNs useinformation gained from previous experience and apply that informationto new problems and/or situations. The ANN uses a “training experience”(i.e., the data set) to build a system of neural interconnects andweighted links between an input layer (i.e., independent variable), ahidden layer of neural interconnects, and an output layer (i.e., thedependant variables or the results). No existing model or knownalgorithmic relationship between these variables is required, but suchrelationships may be used to train the ANN. An initial determination forthe output variables in the training exercise is compared with theactual values in a training data set. Differences are back-propagatedthrough the ANN to adjust the weighting of the various neuralinterconnects, until the differences are reduced to the user's errorspecification. Due largely to the flexibility of the learning algorithm,non-linear dependencies between the input and output layers, can be“learned” from experience.

Several references disclose various methods for using ANNs to solvevarious drilling, production, and formation evaluation problems. Thesereferences include U.S. Pat. No. 6,044,325 issued to Chakravarthy, etal., U.S. Pat. No. 6,002,985 issued to Stephenson, et al., U.S. Pat. No.6,021,377 issued to Dubinsky, et al., U.S. Pat. No. 5,730,234 issued toPutot, U.S. Pat. No. 6,012,015 issued to Tubel, and U.S. Pat. No.5,812,068 issued to Wisler, et al.

However, one skilled in the art will recognize that optimizationpredictions from these methods may not be as accurate as simulations ofdrilling, which may be better equipped to make predictions for eachunique situation.

Simulation methods have been previously introduced which characterizeeither the interaction of a bit with the bottom hole surface of awellbore or the dynamics of BHA.

One simulation method for characterizing interaction between a rollercone bit and an earth formation is described in U.S. Pat. No. 6,516,293(“the '293 patent”), entitled “Method for Simulating Drilling of RollerCone Bits and its Application to Roller Cone Bit Design andPerformance,” and assigned to the assignee of the present invention. The'293 patent discloses methods for predicting cutting element interactionwith earth formations. Furthermore, the '293 patent discloses types ofexperimental tests that can be performed to obtain cuttingelement/formation interaction data. The '293 patent is herebyincorporated by reference in its entirety. Another simulation method forcharacterizing cutting element/formation interaction for a roller conebit is described in Society of Petroleum Engineers (SPE) Paper No. 29922by D. Ma et al., entitled, “The Computer Simulation of the InteractionBetween Roller Bit and Rock”.

Methods for optimizing tooth orientation on roller cone bits aredisclosed in PCT International Publication No. WO00/12859 entitled,“Force-Balanced Roller-Cone Bits, Systems, Drilling Methods, and DesignMethods” and PCT International Publication No. WO00/12860 entitled,“Roller-Cone Bits, Systems, Drilling Methods, and Design Methods withOptimization of Tooth Orientation.

Similarly, SPE Paper No. 15618 by T. M. Warren et al., entitled “DragBit Performance Modeling” discloses a method for simulating theperformance of PDC bits. Also disclosed are methods for defining the bitgeometry and methods for modeling forces on cutting elements and cuttingelement wear during drilling based on experimental test data. Examplesof experimental tests that can be performed to obtain cuttingelement/earth formation interaction data are also disclosed.Experimental methods that can be performed on bits in earth formationsto characterize bit/earth formation interaction are discussed in SPEPaper No. 15617 by T. M. Warren et al., entitled “Laboratory DrillingPerformance of PDC Bits”.

Present systems for optimizing drilling parameters, as described above,focus on either optimizing drilling components or optimizing drillingconditions. Drilling components may be optimized by tailoring suchcomponents for specific well conditions. During such design processes,drill bits, BHAs, drillstrings, and/or drilling tool assemblies may besimulated and adjusted according to the anticipated formation thedrilling tool will be drilling. These design processes may involvecomplex simulations including three dimensional modeling, finite elementanalysis, and/or graphical representations. Such design processes mayrequire vast amounts of time that, while still in the design andmanufacturing stage may be readily available. However, while drilling awellbore, when downhole conditions change, or when the formationdeviates from the anticipated structure, even optimized components mayfail or be less efficient than predicted.

During drilling operations, drilling operators may rely on historicaldata sets, offset well formation data, monitored downhole drillingconditions, and personal experience to anticipate and/or determine whena wellbore condition has changed. A drilling operator may decide tochange drilling parameters (e.g., axial load, rotational speed, drillingfluid flow rate, etc.) in response to changing downhole conditions.However, the drilling operator's response may be based on a limitednumber of options and/or experiences. Alternatively, the drillingoperator may research the given conditions, and base a drillingparameter adjustment on such research. However, during drilling, runningprograms that calculate optimized drilling parameter adjustment are timeintensive and may result in substantial rig downtime.

Traditionally, the optimization of drilling components has involved thefinite knowledge of a drilling operator when designing and assemblingindividual drilling components. Examples of such optimization practicesmay have previously included a drilling operator selecting a drill bit,reamers, spacers, vibration dampeners, and other drilling componentsbased on their individual experience with such devices. The drillingoperator, using their own limited experience then assembled such devicesaccording to their experience, and the drilling assembly was used todrill a wellbore. However, more recently, advances in drillingoptimization programs have allowed a drilling operators own experienceto be supplemented with external experience and historical data. Theprogression of such optimization programs currently allows a drillingoperator to run a simulation of numerous drilling components, asdescribed above, thereby providing for an end product that is furtheroptimized to drill in a specified formation.

Such a computer assisted drilling optimization program may allow anoperator to supplement their own knowledge with the knowledge of otherdrilling assembly designers, experience data from other wells, off setwell data, historical bit runs, or data based on simulated drill runs.Using a computer assisted optimization system, the drilling operator maynow input known and/or expected formation variables (e.g., formationtype), along with their personal experience data (e.g., a starting pointfor a drilling assembly, including bit type, or desirable drillingcomponents), and allow the computer optimization system to iterativelydetermine the optimized components of a drilling assembly. Such systemsprovide for drill assemblies that may be optimized for a givenformation, but are still constrained by the experience data of the humanoperator. Because the computer simulation necessarily begins with theconstrained knowledge of the human drilling operator, the iterativeprocess may initially involve many repetitive, and in certain instancesneedless operations to remove the constraints of the human operator fromlimiting the optimized drilling assembly.

For example, when a drilling operator beings a simulation of a drillingassembly, the drilling operator may initially provide the computeroptimization program formation variable and when they believe to be anoptimized drilling assembly. The simulation program then iterativelysimulates the preselected drilling assembly a number of times, makingsmall changes in the design of the drilling assembly to optimize suchassembly according to the formation variables provided by the humanoperator. However, in certain instances, the drill bit initiallyselected by the drilling operator may be substantially not optimized forthe selected formation. As such, the computer optimization system beginsits simulation based on an incorrect assumption (i.e., the drillingassembly selected by the drilling operator). Because the human operatorhas supplied incorrect initial constraints to the system, the computeroptimization system may either take much time to arrive at an optimizeddrilling assembly, thereby wasting valuable resources and time or, incertain instances, never arrive at an optimized assembly.

While current computer assisted optimization systems used in designingdrilling assemblies may provide for relatively optimized components,because the methods are based on initial human constraints, the systemsare inefficient. Thus, there exists a need for a drilling assemblyoptimization system to guide the design of a drilling assembly toachieve an optimized drilling assembly using a minimum number ofsimulations. Furthermore, there exists a continuing need for a drillassembly optimization system to control guide adjustments to thedrilling assembly throughout the drilling process.

SUMMARY OF THE DISCLOSURE

In one aspect, embodiments disclosed herein relate to a method ofoptimizing a drilling tool assembly including inputting well data intoan optimization system, the optimization system having an experiencedata set and an artificial neural network. The method further includingcomparing the well data to the experience data set and developing aninitial drilling tool assembly based on the comparing the well data tothe experience data, wherein the drilling tool assembly is developedusing the artificial neural network. Additionally, the method includingsimulating the initial drilling tool assembly in the optimization systemand creating result data in the optimization system based on thesimulating.

In another aspect, embodiments disclosed herein relate to a method ofdesigning a drilling tool assembly including inputting well data into anoptimization system, the optimization system including an experiencedata set and an artificial neural network. The method further includingdeveloping an initial drilling tool assembly based on the comparing thewell data to the experience data, wherein the initial drilling toolassembly is developed using the artificial neural network, andsimulating the drilling assembly in the optimization system.Furthermore, the method includes determining a vibrational signature ofthe initial drilling tool assembly and adjusting the initial drillingtool assembly based on the vibrational signature to produce an adjusteddrilling tool assembly.

Other aspects and advantages of the disclosure will be apparent from thefollowing description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of a drilling system.

FIG. 2 is a perspective-view drawing of a fixed-cutter bit.

FIG. 3 is a perspective-view drawing of a roller cone bit.

FIG. 4A is a flowchart diagram of a method of designing a drilling toolassembly in accordance with embodiments of the present disclosure.

FIG. 4B is a flowchart diagram of a method of optimizing drilling toolassemblies according to the method of FIG. 4B in accordance withembodiments of the present disclosure.

FIG. 5 is a flowchart diagram of a method of training an artificialneural network in accordance with embodiments of the present disclosure.

FIG. 6 is a flowchart diagram of a method of loading experience datainto a drilling tool assembly optimization system in accordance withembodiments of the present disclosure.

FIG. 7 is a flowchart diagram of a method of designing drilling toolassemblies in accordance with embodiments of the present disclosure.

FIG. 8 is a flowchart diagram of a method of storing drilling toolassembly result data in accordance with embodiments of the presentdisclosure.

FIG. 9A-D are flowchart diagrams of methods to identify designparameters for a drilling tool assembly in accordance with embodimentsof the present disclosure.

FIG. 9E is a visual representation in accordance with an embodiment ofthe present disclosure.

FIG. 10 is a flowchart diagram of a method of generating a drilling toolassembly order in accordance with embodiments of the present disclosure.

FIG. 11 is a schematic diagram of a drilling communications system inaccordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments disclosed herein are related generally to the field of welldrilling. More specifically, embodiments disclosed herein relate tomethods of optimizing drilling tool assemblies for use in well drillingoperations. More specifically still, embodiments disclosed herein relateto methods of optimizing drilling tool assemblies using artificialneural networks.

The following discussion contains definitions of several specific termsused in this disclosure. These definitions are intended to clarify themeanings of the terms used herein. It is believed that the terms areused in a manner consistent with their ordinary meaning, but thedefinitions are nonetheless specified here for clarity.

The term “offset well formation information” refers to formation datathat is obtained from drilling an offset well in the vicinity of theformation that is being drilled.

The term “historical formation information” refers to formation datathat has been obtained prior to the start of drilling for the formationthat is being drilled. It may include, for example, information relatedto a well drilled in the same general area as the current well,information related to a well drilled in a geologically similar area, orseismic or other survey data.

The term “experience data” may refer to data about a particular drillingcondition, formation, constraint, or simulation. As such, experiencedata may include offset well formation information, historical formationinformation, prior simulation data, and/or other data sources gainedfrom or useful in drilling operations.

The term “drilling parameter” is any parameter that affects the way inwhich the well is being drilled. For example, the WOB is an importantparameter affecting the drilling well. Other drilling parameters includetorque-on-bit (“TOB”), rotary speed of the drill bit (“RPM”), and mudflow rate. There are numerous other drilling parameters, as is known inthe art, and the term is meant to include any such parameter.

Referring to FIG. 4A, a flowchart diagram of a method of designingdrilling tool assemblies in accordance with embodiments of the presentdisclosure is shown. Generally, drilling tool assembly designoptimization includes altering drilling tool assembly components toproduce a drilling tool optimized to perform in a specified formation.Examples of criteria that may be used to measure drilling tooloptimization may include, for example, a vibrational signature of adrilling tool assembly, a ROP of a drilling tool assembly, a cuttingelement wear rate, a dull condition of a drill bit, and other measuresof optimization that may be used for a certain drilling operation. Insome embodiments, drilling tool optimization may include optimizationaccording to multiple criteria, such that a resultant drilling toolassembly is optimized to produce, for example, a specified vibrationalsignature and a desirable ROP.

The criteria for determining a drilling tool assembly optimization maybe measured according to the preference of a drilling engineer and/oraccording to the requirements of a drilling operation. As such, specificoptimization parameters may vary based on the specificities of thecorresponding drilling operation. For example, in an operation where alowest possible vibrational signature is desirable, a drilling engineermay optimize a drilling tool assembly based on a vibrational signaturecalculation, as is described below. However, those of ordinary skill inthe art will appreciate that in other drilling operations, a drillingengineer may be more concerned with optimizing a drilling tool assemblyto produce the highest ROP. In such an embodiment, the drilling toolassembly may be optimized such that the components selected from thedrilling tool assembly, as well as corresponding drilling parameters,are adjusted to produce the highest ROP. Additionally, in someembodiments, drilling engineers may desire that a drilling tool assemblyis optimized with regard to both achieving a highest possible ROP whilemaintaining a low vibrational signature. In such embodiments, a drillingoptimization system may be instructed to consider multiple parameterswhen optimizing the drilling tool assembly. Examples of such parametersmay include a resultant vibrational signature, a desired ROP, a RPM, aWOB, a TOB, and specific drilling tool assembly components. Those ofordinary skill in the art will appreciate that additional aspects ofdrilling may also be parameters used to determine optimization, such as,for example, drilling fluids, drilling fluid flow rates, well type,location of drilling assembly components on the drill string, BHAcomponents, and drill string component material properties.

Thus, the drilling tool assembly optimization methods described beloware exemplary of how drilling tool assemblies may be designed inaccordance with embodiments of the present disclosure. Those of ordinaryskill in the art will appreciate that additional methods of optimizingdrilling tool assemblies may fall within the scope of the presentdisclosure, and as such, the disclosure should not be limited by thedescriptions illustrated herein.

Referring back to FIG. 4A, in this embodiment, the method of designingdrilling tool assembles initially includes inputting well data into anoptimization system 51. Well data may include specific formationinformation known about the drilling location, and/or may also includepredicted formation information. Additional examples of well data thatmay be inputted by a drilling engineer into the optimization system mayinclude known and/or predicted compressive rock strengths of theformation, anticipated wellbore trajectory, and well type.

For example, in the embodiments shown, a drilling engineer may know thecompressive strength of the rock type being drilled. Such data may beavailable from offset wells or regional studies, and may also includepredicted data based on field experience and/or surveys. The drillingengineer may also know the type of well being drilled (e.g., vertical,horizontal, or directional), and may know the anticipated depth of thewell. Well data such as those listed above, along with additional dataas may be known by the drilling engineer, is input into the optimizationsystem 51.

Along with the input of well data, experience data is also input intothe optimization system 52. Experience data may include historical bitrun data, offset well formation data, and prior simulation data. Incertain embodiments, the experience data may also include data compiledfrom other drilling optimization systems and/or data obtained from drilltool assembly component analysis. Examples of such data may be obtainedfrom stored data from simulated bit runs, drilling tool assemblysimulations, drill bit modeling, and/or iterative drilling tool assemblycomponent optimization systems. Those of ordinary skill in the art willappreciate that the experience data set may include multiple sources ofdata combined from, for examples, the groups of experience datadescribed above.

In certain embodiments, the experience data set may be integral to theoptimization system. In such an embodiment, the experience data set maybe loaded as part of the optimization system, and the data may beassembled as matrices of searchable data and stored media in the system.Alternatively, the experience data may be loaded into the optimizationsystem from an external source. Examples of such external sources mayinclude data stored on readable media, data imported from a remote datastore, or data otherwise uploaded from a remote location. Those ofordinary skill in the art will appreciate that experience data may beuploaded as additional data sources become available. Thus, theexperience data of the optimization system may be dynamicallyupgradeable.

In one aspect, such an upgradeable system may include an integral datastore to save iterative simulations of the drilling tool assembly. Insuch a system, every simulation used in optimizing the drilling toolassembly may be incorporated into the experience data. Thus, eachiteration of the drilling tool assembly would dynamically increase theexperience data set, thereby expanding the robustness of the system.However, those of ordinary skill in the art will appreciate that inaddition to locally processed iterative simulations, in certainembodiments, the optimization system may be remotely connected to otheroptimization systems. In such embodiments, the system may be dynamicallyupgradeable across a network including multiple optimization systems.

In some embodiments, experience data sets may also include datapreviously analyzed using ANNs. In such an embodiment, historical bitruns and previously simulated drilling tool assembly data may have beenpreviously analyzed by an ANN, and organized into matrices weighing inthe interconnect between, for example, a specific formation property andthe success of a drilling tool assembly in that formation. Thus, theexperience data may include data sets that include associated datarepresenting a probability of results if a specified drilling toolassembly component is used in a specified formation according tospecified drilling parameters. Because such associations are alreadyincluded in the experience data sets, the optimization system may moreefficiently generate an optimized drilling assembly.

In other embodiments, the experience data may be partially analyzedusing an ANN; however, additional processing may be required to furtherdevelop the series of neural interconnects and weighed links betweeninput and output variables. For example, an experience data set mayinclude data that has associated a formation, a drilling tool assembly,and a vibrational signature. However, the experience data set may nothave been previously associated with corresponding ROP. Thus, using theestablished interconnects found in other data included in the experiencedata set, the optimization system may further process the experiencedata to produce a more robust model associating a formation, a drillingtool assembly, a vibrational signature, and/or an ROP. Such anembodiment may thereby allow the optimization system to generate anoptimized drilling tool assembly with increased efficiency. Those ofordinary skill in the art will appreciate that in certain embodiments,the additional data sets may be modified and updated by an included ANN.

Accordingly, the optimization system of the present disclosure includesboth an experience data set, as described above, and an ANN. The ANN isconfigured to receive instructions from a drilling engineer that includeat least well data, as described above, and has access to experiencedata sets. After receiving the well data, the ANN may compare the welldata with the experience data sets 53 and develop an initial drillingassembly 55. Because the ANN has access to the experience data, when theANN selects components for an initial drilling assembly, the initialdrilling assembly developed may be based on the neural interconnectsweighing related inputs and outputs.

For example, a drilling engineer may input well data 51 includingdrilling assembly components that are required to be in the assembly, alist of available alternate components, and in certain aspects, astarting assembly position for the initial simulation. The optimizationsystem then compares the well data with the experience data 53 using theANN, and develops an initial drilling tool assembly to guide theoptimization process 55. The initial drilling assembly is the simulatedby the optimization system 56, creating result data 57 that may bedisplayed to the drill engineer or used by the optimization system infurther simulations. The initial determination of the ANN may be used toadd components to the drilling tool assembly, remove components from thedrilling tool assembly, or move components on the drilling tool assemblyto achieve an optimized condition. The condition, as described above,may include, for example, a vibrational signature, ROP, dull grade, orwear rate. Thus, in one embodiment, the ANN may determine optimizedpositions of drilling tool assembly components on the drill string to,for example, reduce a vibrational signature of the drilling toolassembly. However, in another embodiment, the ANN may iterativelysimulate the drilling tool assembly to optimize not only the placementof components, but also the addition or removal of such components.Additionally, the ANN may be used to further analyze and determineoptimal drilling assembly components for specific drilling parameters.Thus, the ANN driven optimization system may optimize a drillingassembly for a specific drilling operation, and provide suggesteddrilling parameters for use therein. Furthermore, the ANN drivenoptimization system may generate and or incorporate drilling parameterranges for predicting how a drilling tool assembly my drill in, forexample, a specified formation. Because the optimization system mayincorporate prior data into its optimizations, the optimization of adrilling tool assembly may further include optimized drilling parametersto be used with the optimized drilling tool assembly. Thus, theoptimization system may provide a tool for optimizing the drillingoperation by optimizing the drilling tool assembly, the drillingparameters, and provide for parameter adjustments to be used duringactual drilling.

Because the ANN determines an initial drilling tool assembly, whetherthe determination is placement of drilling components on the drillstring or a determination of which components to use, and because theANN has access to experience data, the initial drilling tool assemblymay be relatively closer in design to the optimized drilling toolassembly. Thus, the number of iterative simulations may be decreased,and an optimized drilling tool assembly thereby designed in less time.

Referring now to FIG. 4B, a flowchart diagram of a method of designingdrilling tool assemblies in accordance with embodiments of the presentdisclosure is shown. In this embodiment, the results data that iscreated in a first simulation (57 of FIG. 4A) may then be used by anoptimization system to optimize the drilling tool assembly 58.Optimization of a drilling tool assembly generally includes usingresults data, that may include the results of an initial simulation,results of a comparison of experience data with well data, or an outputof an ANN (e.g., an initial drilling tool assembly design created) as astarting point for an optimization loop. The optimization system, afterrunning a simulation (e.g., 52 of FIG. 4A), adjusts the drilling toolassembly based on the results data 59. Such adjustments may include, forexample, re-positioning of a component of the drilling tool assembly,addition of a component, or adjustment of a drilling parameter. Theadjusted drilling tool assembly is then re-simulated 60, and additionalresults data is created 61.

After adjustment of the drilling tool assembly according to the resultsdata 61, the optimization system may then either begin a newoptimization loop at 58, which may include additional iterativesimulations, or the optimization system may determine that appropriateoptimization has occurred 62. Such a determination of optimization maybe based on reaching an acceptable or required drilling tool assemblyparameter, drilling parameter, or desired outcome, such as a specifiedvibration signature for the drilling tool assembly. Alternatively, adrilling engineer may determine that the drilling tool is sufficientlyoptimized and terminate the optimization. In still other embodiments,the optimization system may be programmed to perform a set number ofsimulations, and accept the results as a sufficiently optimized drillingtool assembly, unless otherwise instructed by the drilling engineer.Because the optimization system may be programmatically driven by anANN, the number of simulations required to reach an optimized drillingtool assembly condition may be substantially decreased. Furthermore,because the ANN driven optimization system becomes more robust with eachiteration, subsequent drilling tool assembly design operations may alsobecome more efficient, as will be described in detail below.

Those of ordinary skill in the art will appreciate that additionalcomponents and aspects of the above described general optimizationsystem and methods are within the scope of the present disclosure.Specific aspects, embodiments, and examples of embodiments of thepresent disclosure are discussed in detail below.

Method of Training an Artificial Neural Network

In certain embodiments, training an ANN prior to drilling tool assemblyoptimization may further increase the neural base of the ANN, andthereby result in a more efficient optimization. Those of ordinary skillin the art will appreciate that ANN training, as described below, is onemethod for training an ANN, including training an ANN for optimizationof a vibrational signature. As such, alternatively trained ANNs, or ANNstrained in the optimization of multiple components or results of adrilling tool assembly may be used in the optimization system.

In general, training an ANN includes providing the ANN with a trainingdata set. A training data set includes known input variables and knownoutput variables that correspond to the input variables. The ANN thenbuilds a series of neural interconnects and weighted links between theinput variables and the output variables. Using this trainingexperience, an ANN may then predict output variables values based on aset of input variables.

To train the ANN to determine formation properties, a training data setmay include known input variables (representing well data, e.g.,previously acquired data) and known output variables (representing theformation properties corresponding to the well data). After training,the ANN may be used to determine unknown formation properties based onmeasured well data. For example, raw current well data may be input to acomputer with a trained ANN. Then, using the trained ANN and the currentwell data, the computer may output estimations of the formationproperties.

Additionally, training an ANN in accordance with the present disclosuremay include providing the ANN with experience data. Thus, in oneembodiment, data collected may be preserved and input into an ANNtraining program. An ANN training program may serve as a collectionlocation for different types of experience data, such as, for example,historical bit run data, optimized bit/BHA studies, optimized drillstring/tool assembly studies, and other studies as are known by those ofordinary skill in the art. The ANN training program may assemble suchdata sources, and develop secondary ANNs that may be used to analyzespecific components of a drilling operation.

Referring to FIG. 5, a flowchart diagram of a method of training an ANNin accordance with an embodiment of the present disclosure is shown. Inone embodiment, an ANN training program 601 may collect and process datafrom a number of different sources including, experience data 602,optimized bit data 603, historical bit run data 604, optimized toolassembly data 605, and empirical well condition data 606. Training ANN601 may collect data from any of the above mentioned sources, processthe data, and produce a trained ANN targeting a specific tool assemblyor wellbore condition. Examples of such trained ANNs may include, avibrational ANN 607, a bit wear ANN 608, a ROP ANN 609, a directionalANN 610 and/or a mud flow rate ANN 611.

Predicting the drilling tool assembly parameters may be accomplishedusing a trained ANN. in such embodiments, the ANN may be trained using atraining data set that includes the experience data and the correlationof well data to offset well data as the inputs and drilling toolassembly parameters and vibrational signatures as the outputs. Using thetraining data set, the ANN may build a series of neural interconnectsand weighted links between the input variables and the output variables.Using this training experience, an ANN may then predict vibrationalresponses for a drilling tool assembly based on inputs of experiencedata or the offset well data and the correlation of the current welldata to the experience data.

As mentioned above, one such type of trained ANN may include avibrational analysis ANN 607. Such an ANN may be useful in analyzing adrilling tool assembly during tool design. Methods for dynamicallysimulating cutting tool and bit vibrations are disclosed in U.S. PatentPublication No. 2005/0273302, titled Dynamically Balanced Cutting ToolSystem, assigned to the assignee of the present invention, andincorporated by reference herein in its entirety. Such calculations andprocesses necessary for the simulation of cutting tool and bitvibrations may be performed during the training of vibrational ANN 607,so that vibrational ANN 607 includes a database of stored drillingconditions and drilling parameters affecting the conditions containedtherein.

After vibrational ANN 607 is trained using experience data, as describedabove, the ANN may be continuously trained during subsequentsimulations. Referring now to FIG. 6, a flowchart diagram of ANNtraining based on simulation data according to one embodiment of thepresent disclosure is shown. After a vibrational ANN is trained usingexperience data 612, historical data 613, offset well data 614, andpreviously acquired simulation data 615, as described above, the ANN maybe continuously trained during subsequent simulations. Additionally,those of ordinary skill in the art will appreciate that experience data612 may include historical data, offset well data, and previoussimulation data not independently input into the ANN or another aspectof the optimization system.

In this embodiment, the neural interconnects created in the ANN duringprocessing of the experience data 616 may allow the ANN to moreefficiently process substantive data sets, including subsequentsimulations and initial drilling tool assembly designs. Furthermore, asan optimized drilling tool assembly is developed 617, the outcomes ofthe simulation, adjusting and optimization may be input into and used asprevious simulation data 615. Thus, each iterative simulation of theoptimization system, including each process of the ANN, may increase theefficiency of the optimization process, because such simulation data maybe saved and used in subsequent iterations or optimization processes.

Those or ordinary skill in the art will appreciate that the neuralinterconnects established by processing experience data 616 andoptimizing the drilling tool assembly 617 may be stored either locally,or in a remote data store, to be accessed and/or used by other ANNsand/or optimization systems. Furthermore, an optimization system thatincludes multiple ANNs may use the previous simulation data asadditional data input into secondary ANNs during ANN training.

In the systems described above, because the ANN is trained in previoussimulations, the ANN may be used to guide resulting simulation processesto achieve a desired condition (e.g., a specified vibrationalsignature). Thus, the iterations in the simulation are controlled by theANN, such that fewer simulations are required to reach the desiredcondition. An ANN guided optimization system may thereby require fewersimulations, less external data input, and less human interaction whenprocessing data in drilling tool assembly operations.

Those of ordinary skill in the art will appreciate that ANN guidedoptimization may be driven by a single ANN or a plurality of ANNsnetworked together, operating in series to determine one optimizeddrilling assembly parameter, or in parallel to determine multipleoptimized drilling assembly parameters. Furthermore, the ANN guidedoptimization may be used in multiple methods of simulating drilling toolassemblies, such that the optimization system determines optimizeddrilling tool assembly parameters, optimized drilling parameters, orother optimized properties of a drilling operation that may effect, forexample, a vibration signature, a wear rate, a dull condition, oranother property that effects the drilling operation. One example of amethod of simulating a drilling tool assembly in accordance withembodiments disclosed herein is described in detail below.

Method of Simulating a Drilling Tool Assembly

Those of ordinary skill in the art will appreciate that optimizing adrilling tool assembly involves an iterative process of simulating andadjusting parameters of the drilling tool assembly according to resultdata generated during a prior simulation. The method of simulating adrilling tool assembly described below is illustrative of how aniterative simulation may occur. Additional aspects of simulation, suchas additional constraints to drilling parameters, drilling tool assemblyparameters, or data available during the simulation may further increasethe efficiency of the system.

Identifying design parameters for use in a drilling tool assembly mayinclude the identification, simulation, and adjustment of components of,among other things, a drill string, drill bit, and/or BHA. The belowdescribed methods for identifying such design parameters for drill bits,drill strings, and/or BHAs may include examples of experience data, asdescribed above, that may be used in accordance with embodiments of thepresent disclosure. Further more, multiple optimization systemsincorporating methods for drill bit, drill string, and/or BHA designoptimization may be combined as multiple nodes of experience data foruse in training, for example, ANNs. Thus, one of ordinary skill in theart will appreciate that the method for simulating a drilling toolassembly described below is merely one method that may be used insimulating a drilling tool assembly.

In one aspect, the present disclosure provides a method for simulatingthe dynamic response of a drilling tool assembly drilling earthformation. Advantageously, this method takes into account interactionbetween the entire drilling tool assembly and the drilling environment.Interaction between the drilling tool assembly and the drillingenvironment may include interaction between the drill bit at the end ofthe drilling tool assembly and the formation at the bottom of thewellbore. Interaction between the drilling tool assembly and thedrilling environment also may include interaction between the drillingtool assembly and the side (or wall) of the wellbore. Further,interaction between the drilling tool assembly and drilling environmentmay include viscous damping effects of the drilling fluid on the dynamicresponse of the drilling tool assembly.

A flow chart for one embodiment of the invention is illustrated in FIG.7. The first step in this embodiment is selecting (defining or otherwiseproviding) parameters 100, including initial drilling tool assemblyparameters 102, initial drilling environment parameters 104, drillingoperating parameters 106, and drilling tool assembly/drillingenvironment interaction information (parameters and/or models) 108. Thenext step involves constructing a mechanics analysis model of thedrilling tool assembly 110. The mechanics analysis model can beconstructed using the drilling tool assembly parameters 102 and Newton'slaw of motion. The next step involves determining an initial staticstate of the drilling tool assembly 112 in the selected drillingenvironment using the mechanics analysis model 110 along with drillingenvironment parameters 104 and drilling tool assembly/drillingenvironment interaction information 108. Once the mechanics analysismodel is constructed and an initial static state of the drill string isdetermined, the resulting static state parameters can be used with thedrilling operating parameters 106 to incrementally solve for the dynamicresponse 114 of the drilling tool assembly 50 to rotational input fromthe rotary table 64 and the hook load provided at the hook 62. Once asimulated response for an increment in time (or for the total time) isobtained, results from the simulation can be provided as output 118, andused to generate a visual representation of drilling if desired.

In one example, illustrated in FIG. 8, incrementally solving for thedynamic response (indicated as 116) may not only include solving themechanics analysis model for the dynamic response to an incrementalrotation, at 120, but may also include determining, from the responseobtained, loads (e.g., drilling environment interaction forces) on thedrilling tool assembly due to interaction between the drilling toolassembly and the drilling environment during the incremental rotation,at 122, and resolving for the response of the drilling tool assembly tothe incremental rotation, at 124, under the newly determined loads. Thedetermining and resolving may be repeated in a constraint update loop128 until a response convergence criterion 126 is satisfied. Once aconvergence criterion is satisfied, the entire incremental solvingprocess 116 may be repeated for successive increments until an endcondition for simulation is reached.

For the example shown in FIGS. 9A-D, the parameters provided as input200 include drilling tool assembly design parameters 202, initialdrilling environment parameters 204, drilling operating parameters 206,and drilling tool assembly/drilling environment interaction parametersand/or models 208.

Drilling tool assembly design parameters 202 may include drill stringdesign parameters, BHA design parameters, and drill bit designparameters. In the example shown, the drill string comprises a pluralityof joints of drill pipe, and the BHA comprises drill collars,stabilizers, bent housings, and other downhole tools (e.g., MWD tools,LWD tools, downhole motor, etc.), and a drill bit. As noted above, whilethe drill bit, generally, is considered a part of the BHA, in thisexample the design parameters of the drill bit are shown separately toillustrate that any type of drill bit may be defined and modeled usingany drill bit analysis model.

Drill string design parameters include, for example, the length, insidediameter (ID), outside diameter (OD), weight (or density), and othermaterial properties of the drill string in the aggregate. Alternatively,drill string design parameters may include the properties of eachcomponent of the drill string and the number of components and locationof each component of the drill string. For example, the length, ID, OD,weight, and material properties of one joint of drill pipe may beprovided along with the number of joints of drill pipe which make up thedrill string. Material properties used may include the type of materialand/or the strength, elasticity, and density of the material. The weightof the drill string, or individual components of the drill string, maybe provided as “weight in drilling fluids” (the weight of the componentwhen submerged in the selected drilling fluid).

BHA design parameters include, for example, the bent angle andorientation of the motor, the length, equivalent inside diameter (ID),outside diameter (OD), weight (or density), and other materialproperties of each of the various components of the BHA. In thisexample, the drill collars, stabilizers, and other downhole tools aredefined by their lengths, equivalent IDs, ODs, material properties,weight in drilling fluids, and position in the drilling tool assembly.

The drill bit design parameters include, for example, the bit type(roller cone, fixed-cutter, etc.) and geometric parameters of the bit.Geometric parameters of the bit may include the bit size (e.g.,diameter), number of cutting elements, and the location, shape, size,and orientation of the cutting elements. In the case of a roller conebit, drill bit design parameters may further include cone profiles, coneaxis offset (offset from perpendicular with the bit axis of rotation),the number of cutting elements on each cone, the location, size, shape,orientation, etc. of each cutting element on each cone, and any otherbit geometric parameters (e.g., journal angles, element spacing, etc.)to completely define the bit geometry. In general, bit, cutting element,and cone geometry may be converted to coordinates and provided as input.One preferred method for obtaining bit design parameters is the use of3-dimensional CAD solid or surface models to facilitate geometric input.Drill bit design parameters may further include material properties,such as strength, hardness, etc., of components of the bit.

Initial drilling environment parameters 204 include, for example,wellbore parameters. Wellbore parameters may include wellbore trajectory(or geometric) parameters and wellbore formation parameters. Wellboretrajectory parameters may include an initial wellbore measured depth (orlength), wellbore diameter, inclination angle, and azimuth direction ofthe wellbore trajectory. In the typical case of a wellbore comprisingsegments having different diameters or differing in direction, thewellbore trajectory information may include depths, diameters,inclination angles, and azimuth directions for each of the varioussegments. Wellbore trajectory information may further include anindication of the curvature of the segments (which may be used todetermine the order of mathematical equations used to represent eachsegment). Wellbore formation parameters may include the type offormation being drilled and/or material properties of the formation suchas the formation strength, hardness, plasticity, and elastic modulus.

Drilling operating parameters 206, in this embodiment, include therotary table speed at which the drilling tool assembly is rotated (RPM),the downhole motor speed if a downhole motor is included, and the hookload. Drilling operating parameters 206 may further include drillingfluid parameters, such as the viscosity and density of the drillingfluid, for example. it should be understood that drilling operatingparameters 206 are not limited to these variables. In other embodiments,drilling operating parameters 206 may include other variables, such as,for example, rotary torque and drilling fluid flow rate. Additionally,drilling operating parameters 206 for the purpose of simulation mayfurther include the total number of bit revolutions to be simulated orthe total drilling time desired for simulation. However, it should beunderstood that total revolutions and total drilling time are simply endconditions that can be provided as input to control the stopping pointof simulation, and are not necessary for the calculation required forsimulation. Additionally, in other embodiments, other end conditions maybe provided, such as total drilling depth to be simulated, or byoperator command, for example.

Drilling tool assembly/drilling environment interaction information 208includes, for example, cutting element/earth formation interactionmodels (or parameters) and drilling tool assembly/formation impact,friction, and damping models and/or parameters. Cutting element/earthformation interaction models may include vertical force-penetrationrelations and/or parameters which characterize the relationship betweenthe axial force of a selected cutting element on a selected formationand the corresponding penetration of the cutting element into theformation. Cutting element/earth formation interaction models may alsoinclude lateral force-scraping relations and/or parameters whichcharacterize the relationship between the lateral force of a selectedcutting element on a selected formation and the corresponding scrapingof the formation by the cutting element. Cutting element/formationinteraction models may also include brittle fracture crater modelsand/or parameters for predicting formation craters which will likelyresult in brittle fracture, wear models and/or parameters for predictingcutting element wear resulting from contact with the formation, and coneshell/formation or bit body/formation interaction models and/orparameters for determining forces on the bit resulting from coneshell/formation or bit body/formation interaction. One example ofmethods for obtaining or determining drilling tool assembly/formationinteraction models or parameters can be found in U.S. Pat. No.6,516,293, assigned to the assignee of the present invention andincorporated herein by reference. Other methods for modeling drill bitinteraction with a formation can be found in the previously noted SPEPapers No. 29922, No. 15617, and No. 15618, and PCT InternationalPublication Nos. WO 00/12859 and WO 00/12860.

Drilling tool assembly/formation impact, friction, and damping modelsand/or parameters characterize impact and friction on the drilling toolassembly due to contact with the wall of the wellbore and the viscousdamping effects of the drilling fluid. These models/parameters include,for example, drill string-BHA/formation impact models and/or parameters,bit body/formation impact models and/or parameters, drillstring-BHA/formation friction models and/or parameters, and drillingfluid viscous damping models and/or parameters. One skilled in the artwill appreciate that impact, friction and damping models/parameters maybe obtained through laboratory experimentation, in a method similar tothat disclosed in the prior art for drill bits interactionmodels/parameters. Alternatively, these models may also be derived basedon mechanical properties of the formation and the drilling toolassembly, or may be obtained from literature. Prior art methods fordetermining impact and friction models are shown, for example, in paperssuch as the one by Yu Wang and Matthew Mason, entitled “Two-DimensionalRigid-Body Collisions with Friction”, Journal of Applied Mechanics,September 1992, Vol. 59, pp. 635-642.

As shown in FIGS. 9A-D, once input parameters/models 200 are selected,determined, or otherwise provided, a two-part mechanics analysis modelof the drilling tool assembly is constructed and used to determine theinitial static state (at 232) of the drilling tool assembly in thewellbore. The first part of the mechanics analysis model takes intoconsideration the overall structure of the drilling tool assembly, withthe drill bit being only generally represented. In this embodiment, forexample, a finite element method is used (generally described at 212)wherein an arbitrary initial state (such as hanging in the vertical modefree of bending stresses) is defined for the drilling tool assembly as areference and the drilling tool assembly is divided into N elements ofspecified element lengths (i.e., meshed). The static load vector foreach element due to gravity is calculated. Then element stiffnessmatrices are constructed based on the material properties (e.g.,elasticity), element length, and cross sectional geometrical propertiesof drilling tool assembly components provided as input and are used toconstruct a stiffness matrix, at 212, for the entire drilling toolassembly (wherein the drill bit is generally represented by a singlenode). Similarly, element mass matrices are constructed by determiningthe mass of each element (based on material properties, etc.) and areused to construct a mass matrix, at 214, for the entire drilling toolassembly. Additionally, element damping matrices can be constructed(based on experimental data, approximation, or other method) and used toconstruct a damping matrix, at 216, for the entire drilling toolassembly. Methods for dividing a system into finite elements andconstructing corresponding stiffness, mass, and damping matrices areknown in the art and thus are not explained in detail here. Examples ofsuch methods are shown, for example, in “Finite Elements for Analysisand Design” by J. E. Akin (Academic Press, 1994).

The second part of the mechanics analysis model of the drilling toolassembly is a mechanics analysis model of the drill bit which takes intoaccount details of selected drill bit design. The drill bit mechanicsanalysis model is constructed by creating a mesh of the cutting elementsand cones (for a roller cone bit) of the bit, and establishing acoordinate relationship (coordinate system transformation) between thecutting elements and the cones, between the cones and the bit, andbetween the bit and the tip of the BHA. As previously noted, examples ofmethods for constructing mechanics analysis models for roller cone drillbits can be found in U.S. Pat. No. 6,516,293, as well as SPE Paper No.29922, and PCT International Publication Nos. WO 00/12859 and WO00/12860, noted above.

Because the response of the drilling tool assembly is subject to theconstraint within the wellbore, wellbore constraints for the drillingtool assembly are determined, at 222, 224. First, the trajectory of thewall of the wellbore, which constrains the drilling tool assembly andforces it to conform to the wellbore path, is constructed at 220 usingwellbore trajectory parameters provided as input at 204. For example, acubic B-spline method or other interpolation method can be used toapproximate wellbore wall coordinates at depths between the depthsprovided as input data. The wall coordinates are then discretized (ormeshed), at 224 and stored. Similarly, an initial wellbore bottomsurface geometry, which is either selected or determined, may also bediscretized, at 222, and stored. The initial bottom surface of thewellbore may be selected as flat or as any other contour, which may beprovided as wellbore input at 204 or 222. Alternatively, the initialbottom surface geometry may be generated or approximated based on theselected bit geometry. For example, the initial bottomhole geometry maybe selected from a “library” (i.e., database) containing storedbottomhole geometries resulting from the use of various bits.

In this embodiment, a coordinate mesh size of 1 millimeter is selectedfor the wellbore surfaces (wall and bottomhole); however, the coordinatemesh size is not intended to be a limitation on the invention. Oncemeshed and stored, the wellbore wall and bottomhole geometry, together,comprise the initial wellbore constraints within which the drilling toolassembly must operate, thus, within which the drilling tool assemblyresponse must be constrained.

As shown in FIGS. 9A-D, once the (two-part) mechanics analysis model forthe drilling tool assembly is constructed (using Newton's second law)and the wellbore constraints are specified 222, 224, the mechanics modeland constraints can be used to determine the constraint forces on thedrilling tool assembly when forced to the wellbore trajectory andbottomhole from its original “stress free” state. In this embodiment,the constraint forces on the drilling tool assembly are determined byfirst displacing and fixing the nodes of the drilling tool assembly sothe centerline of the drilling tool assembly corresponds to thecenterline of the wellbore, at 226. Then, the corresponding constrainingforces required on each node (to fix it in this position) are calculatedat 228 from the fixed nodal displacements using the drilling toolassembly (i.e., system or global) stiffness matrix from 212. Once the“centerline” constraining forces are determined, the hook load isspecified, and initial wellbore wall constraints and bottomholeconstraints are introduced at 230 along the drilling tool assembly andat the bit (lowest node). The centerline constraints are used as thewellbore wall constraints. The hook load and gravitational force vectorare used to determine the WOB.

As previously noted, the hook load is the load measured at the hook fromwhich the drilling tool assembly is suspended. Because the weight of thedrilling tool assembly is known, the bottomhole constraint force (i.e.,WOB) can be determined as the weight of the drilling tool assembly minusthe hook load and the frictional forces and reaction forces of the holewall on the drilling tool assembly.

Once the initial loading conditions are introduced, the “centerline”constraint forces on all of the nodes are removed, a gravitational forcevector is applied, and the static equilibrium position of the assemblywithin the wellbore is determined by iteratively calculating the staticstate of the drilling tool assembly 232. Iterations are necessarybecause the contact points for each iteration may be different. Theconvergent static equilibrium state is reached and the iteration processends when the contact points and, hence, contact forces aresubstantially the same for two successive iterations. Along with thestatic equilibrium position, the contact points, contact forces,friction forces, and static WOB on the drilling tool assembly aredetermined. Once the static state of the system is obtained (at 232) itcan be used as the staring point (initial condition) 234 for simulationof the dynamic response of the drilling tool assembly drilling earthformation.

As shown in FIGS. 9A-D, once input data are provided and the staticstate of the drilling tool assembly in the wellbore is determined,calculations in the dynamic response simulation loop may be carried out.Briefly summarizing the functions performed in the dynamic responseloop, the drilling tool assembly drilling earth formation is simulatedby “rotating” the top of the drilling tool assembly (and the downholemotor, if used) through an incremental angle (at 242), and thencalculating the response of the drilling tool assembly under thepreviously determined loading conditions 244 to the rotation(s). Theconstraint loads on the drilling tool assembly resulting frominteraction with the wellbore wall during the incremental rotation areiteratively determined (in loop 245) and are used to update the drillingtool assembly constraint loads (i.e., global load vector), at 248, andthe response is recalculated under the updated loading condition. Thenew response is then rechecked to determine if wall constraint loadshave changed and, if necessary, wall constraint loads are re-determined,the load vector updated, and a new response calculated. Then thebottomhole constraint loads resulting from bit interaction with theformation during the incremental rotation are evaluated based on the newresponse (loop 252), the load vector is updated (at 279), and a newresponse is calculated (at 280). The wall and bottomhole constraintforces are repeatedly updated (in loop 285) until convergence of adynamic response solution is determined (i.e., changes in the wallconstraints and bottomhole constraints for consecutive solutions aredetermined to be negligible). The entire dynamic simulation loop is thenrepeated for successive incremental rotations until an end condition ofthe simulation is reached (at 290) or until simulation is otherwiseterminated. A more detailed description of the elements in thesimulation loop follows.

Prior to the start of the simulation loop, drilling operating parameters206 are specified. As previously noted, the drilling operatingparameters 206 include the rotary table speed, downhole motor speed (ifincluded in the BHA), and the hook load. In this example, the endcondition for simulation is also provided at 204, as either the totalnumber of revolutions to be simulated or the total time for thesimulation. Additionally, the incremental step desired for calculationsshould be defined, selected, or otherwise provided. In the embodimentshown, an incremental time step of Δt=10⁻³ seconds is selected. However,it should be understood that the incremental time step is not intendedto be a limitation on the invention.

Once the static state of the system is known (from 232) and theoperational parameters are provided, the dynamic response simulationloop 240 can begin. In the first step of the simulation loop 240, thecurrent time increment is calculated at 241, wherein t_(i+1)=t_(i)+Δt.Then, the incremental rotation which occurs during that time incrementis calculated, at 242. In this embodiment, the formula used to calculatean incremental rotation angle at time t_(i+1) is θ_(i+1)=θ_(i)+RPM *Δt*60, wherein RPM is the rotational speed (in RPM) of the rotary tableprovided as input data (at 204). The calculated incremental rotationangle is applied proximal to the top of the drilling tool assembly (atthe node(s) corresponding to the position of the rotary table). If adownhole motor is included in the BHA, the downhole motor incrementalrotation is also calculated and applied to the corresponding nodes.

Once the incremental rotation angle and current time are determined, thesystem's new configuration (nodal positions) under the extant loads andthe incremental rotation is calculated (at 244) using mechanics analysismodel modified to include the rotational input as an excitation. Forexample, a direct integration scheme can be used to solve the resultingdynamic equilibrium equations (modified mechanics analysis model) forthe drilling tool assembly. The dynamic equilibrium equation (like themechanics analysis equation) can be derived using Newton's second law ofmotion, wherein the constructed drilling tool assembly mass, stiffness,and damping matrices along with the calculated static equilibrium loadvector can be used to determine the response to the incrementalrotation. For the example shown in FIGS. 9A-D, it should be understoodthat at the first time increment t₁ the extant loads on the system arethe static equilibrium loads (calculated for t₀) which include thestatic state WOB and the constraint loads resulting from drilling toolassembly contact with the wall and bottom of the wellbore.

As the drilling tool assembly is incrementally “rotated”, constraintloads acting on the bit may change. For example, points of the drillingtool assembly in contact with the borehole surface prior to rotation maybe moved along the surface of the wellbore resulting in friction forcesat those points. Similarly, some points of the drilling tool assembly,which were nearly in contact with the borehole surface prior to theincremental rotation, may be brought into contact with the formation asa result of the incremental rotation, resulting in impact forces on thedrilling tool assembly at those locations. As shown in FIGS. 9A-D,changes in the constraint loads resulting from the incremental rotationof the drilling tool assembly can be accounted for in the wallinteraction update loop 245.

In this example, once the system's response (i.e., new configuration)under the current loading conditions is obtained, the positions of thenodes in the new configuration are checked (at 244) in the wallconstraint loop 245 to determine whether any nodal displacements falloutside of the bounds (i.e., violate constraint conditions) defined bythe wellbore wall. If nodes are found to have moved outside of thewellbore wall, the impact and/or friction forces which would haveoccurred due to contact with the wellbore wall are approximated forthose nodes (at 248) using the impact and/or friction models orparameters provided as input at 208. Then the global load vector for thedrilling tool assembly is updated (also shown at 208) to reflect thenewly determined constraint loads. Constraint loads to be calculated maybe determined to result from impact if, prior to the incrementalrotation, the node was not in contact with the wellbore wall. Similarly,the constraint load can be determined to result from frictional drag ifthe node now in contact with the wellbore wall was also in contact withthe wall prior to the incremental rotation. Once the new constraintloads are determined and the global load vector is updated, at 248, thedrilling tool assembly response is recalculated (at 244) for the sameincremental rotation under the newly updated load vector (as indicatedby loop 245). The nodal displacements are then rechecked (at 246) andthe wall interaction update loop 245 is repeated until a dynamicresponse within the wellbore constraints is obtained.

Once a dynamic response conforming to the borehole wall constraints isdetermined for the incremental rotation, the constraint loads on thedrilling tool assembly due to interaction with the bottomhole during theincremental rotation are determined in the cone interaction loop 250.Those skilled in the art will appreciate that any method for modelingdrill bit/earth formation interaction during drilling may be used todetermine the forces acting on the drill bit during the incrementalrotation of the drilling tool assembly. An example of one method isillustrated in the cone interaction loop 250 in FIGS. 9A-D.

In the cone interaction loop 250, the mechanics analysis model of thedrill bit is subjected to the incremental rotation angle calculated forthe lowest node of the drilling tool assembly, and is then movedlaterally and vertically to the new position obtained from the samecalculation, as shown at 249. As previously noted, the drill bit in thisexample is a roller cone drill bit. Thus, in this example, once the bitrotation and new bit position are determined, interaction between eachcone and the formation is determined. For a first cone, an incrementalcone rotation angle is calculated at 252 based on a calculatedincremental cone rotation speed and used to determine the movement ofthe cone during the incremental rotation. It should be understood thatthe incremental cone rotation speed can be determined from all theforces acting on the cutting elements of the cone and Newton's secondlaw of motion. Alternatively, it may be approximated from the rotationspeed of the bit and the effective radius of the “drive row” of thecone. The effective radius is generally related to the lateral extent ofthe cutting elements that extend the farthest from the axis of rotationof the cone. Thus, the rotation speed of the cone can be defined orcalculated based on the calculated bit rotational speed and the definedgeometry of the cone provided as input (e.g., the cone diameter profile,cone axial offset, etc).

Then, for the first cone, interaction between each cutting element andthe earth formation is determined in the cutting element/formationinteraction loop 256. In this interaction loop 256, the new position ofa cutting element, for example, cutting element j on row k, iscalculated 258 based on the incremental cone rotation and bit rotationand translation. Then, the location of cutting element j,k relative tothe bottomhole and wall of the wellbore is evaluated, at 259, todetermine whether cutting element interference (or contact) with theformation occurred during the incremental rotation of the bit. If it isdetermined that contact did not occur, then the next cutting element isanalyzed and the interaction evaluation is repeated for the next cuttingelement. If contact is determined to have occurred, then a depth ofpenetration, interference projection area, and scraping distance of thecutting element in the formation are determined, at 262, based on thenext movement of the cutting element during the incremental rotation.The depth of penetration is the distance from the earth formationsurface a cutting element penetrates into the earth formation. Depth ofpenetration can range from zero (no penetration) to the full height ofthe cutting element (full penetration). Interference projection area isthe fractional amount of the cutting element surface area, correspondingto the depth of penetration, which actually contacts the earthformation. A fractional amount of contact usually occurs due to cratersin the formation formed from previous contact with cutting elements.Scraping distance takes into account the movement of the cutting elementin the formation during the incremental rotation. Once the depth ofpenetration, interference projection area, and scraping distance aredetermined for cutting element j,k these parameters are used inconjunction with the cutting element/formation interaction data todetermine the resulting forces (constraint forces) exerted on thecutting element by the earth formation (also indicated at 262). Forexample, force may be determined using the relationship disclosed inU.S. Pat. No. 6,516,293, noted above and incorporated herein byreference.

Once the cutting element/formation interaction variables (area, depth,force, etc.)

are determined for cutting element j, k, the geometry of the bottomsurface of the wellbore can be temporarily updated, at 264, to reflectthe removal of formation by cutting element j,k during the incrementalrotation of the drill bit. The actual size of the crater resulting fromcutting element contact with the formation can be determined from thecutting element/earth formation interaction data based on the bottomholesurface geometry, and the forces exerted by the cutting element. Onesuch procedure is described in U.S. Pat. No. 6,516,293, noted above.

After the bottomhole geometry is temporarily updated, insert wear andstrength can also be analyzed, as shown at 270, based on wear models andcalculated loads on the cutting elements to determine wear on thecutting elements resulting from contact with the formation and theresulting reduction in cutting element strength. Then, the cuttingelement/formation interaction loop 260 calculations are repeated for thenext cutting element (j=j+1) of row k until cutting element/formationinteraction for each cutting element of the row is determined.

Once the forces on each cutting element of a row are determined, thetotal forces on that row are calculated (at 268) as a sum of all theforces on the cutting elements of that row. Then, the cuttingelement/earth formation interaction calculations are repeated for thenext row on the cone (k−k+1) (in the row interaction loop 269) until theforces on each of the cutting elements on each of the rows on that coneare obtained. Once interaction of all of the cutting elements on a coneis determined, cone shell interaction with the formation is determinedby checking node displacements at the cone surface, at 270, to determineif any of the nodes are out of bounds with respect to (or make contactwith) the wellbore wall or bottomhole surface. If cone shell contact isdetermined to have occurred for the cone during the incrementalrotation, the contact area and depth of penetration of the cone shellare determined (at 272) and used to determine interaction forces on thecone shell resulting from the contact.

Once forces resulting from cone shell contact with the formation duringthe incremental rotation are determined, or it is determined that noshell contact has occurred, the total interaction forces on the coneduring the incremental rotation can be calculated by summing all of therow forces and any cone shell forces on the cone, at 274. The totalforces acting on the cone during the incremental rotation may then beused to calculate the incremental cone rotation speed {dot over(θ)}_(i), at 276. Cone interaction calculations are then repeated foreach cone (l=l+1) until the forces, rotation speed, etc. on each of thecones of the bit due to interaction with the formation are determined.

Once the interaction forces on each cone are determined, the total axialforce on the bit (dynamic WOB) during the incremental rotation of thedrilling tool assembly is calculated 278, from the cone forces. Thenewly calculated bit interaction forces are then used to update theglobal load vector (at 279), and the response of the drilling toolassembly is recalculated (at 280) under the updated loading condition.The newly calculated response is then compared to the previous response(at 282) to determine if the responses are substantially similar. If theresponses are determined to be substantially similar, then the newlycalculated response is considered to have converged to a correctsolution. However, if the responses are not determined to besubstantially similar, then the bit interaction forces are recalculatedbased on the latest response at 284 and the global load vector is againupdated (as indicated at 284). Then, a new response is calculated byrepeating the entire response calculation (including the wellbore wallconstraint update and drill bit interaction force update) untilconsecutive responses are obtained which are determined to besubstantially similar (indicated by loop 285), thereby indicatingconvergence to the solution for dynamic response to the incrementalrotation.

Once the dynamic response of the drilling tool assembly to anincremental rotation is obtained from the response force update loop285, the bottomhole surface geometry is then permanently updated (at286) to reflect the removal of formation corresponding to the solution.At this point, output information desired from the incrementalsimulation step can be provided as output or stored. For example, thenew position of the drilling tool assembly, the dynamic WOB, coneforces, cutting element forces, impact forces, friction forces, may beprovided as output information or stored.

This dynamic response simulation loop 240 as described above is thenrepeated for successive incremental rotations of the bit until an endcondition of the simulation (checked at 290) is satisfied. For example,using the total number of bit revolutions to be simulated as thetermination command, the incremental rotation of the drilling toolassembly and subsequent iterative calculations of the dynamic responsesimulation loop 240 will be repeated until the selected total number ofrevolutions to be simulated is reached. Repeating the dynamic responsesimulation loop 240 as described above will result in simulating theperformance of an entire drilling tool assembly drilling earthformations with continuous updates of the bottomhole pattern as drilled,thereby simulating the drilling of the drilling tool assembly in theselected earth formation. Upon completion of a selected number ofoperations of the dynamic response simulation loop, results of thesimulation may be used to generate output information at 294characterizing the performance of the drilling tool assembly drillingthe selected earth formation under the selected drilling conditions, asshown in FIGS. 9A-D. It should be understood that the simulation can bestopped using any other suitable termination indicator, such as aselected wellbore depth desired to be drilled, indicated divergence of asolution, etc.

As noted above, output information from a dynamic simulation of adrilling tool assembly drilling an earth formation may include, forexample, the drilling tool assembly configuration (or response) obtainedfor each time increment, and corresponding bit forces, cone forces,cutting element forces, impact forces, friction forces, dynamic WOB,resulting bottomhole geometry, etc. This output information may bepresented in the form of a visual representation (indicated at 294),such as a visual representation of the borehole being drilled throughthe earth formation with continuous updated bottomhole geometries andthe dynamic response of the drilling tool assembly to drilling presentedon a computer screen. Alternatively, the visual representation mayinclude graphs of parameters provided as input and/or calculated duringthe simulation. For example, a time history of the dynamic WOB or thewear of cutting elements during drilling may be presented as a graphicdisplay on a computer screen. It should be understood that the inventionis not limited to any particular type of display. Further, the meansused for visually displaying aspects of simulated drilling is a matterof convenience for the system designer, and is not intended to limit thepresent disclosure. One example of output information converted to avisual representation is illustrated in FIG. 9E, wherein the rotation ofthe drilling tool assembly and corresponding drilling of the formationis graphically illustrated as a visual display of drilling and desiredparameters calculated during drilling can be numerically displayed.

The example described above represents only one embodiment of thepresent disclosure. Those skilled in the art will appreciate that otherembodiments can be devised which do not depart from the scope of thedisclosure as described herein. For example, an alternative method canbe used to account for changes in constraint forces during incrementalrotation. For example, instead of using a finite element method, afinite difference method or a weighted residual method can be used tomodel the drilling tool assembly. Similarly, other methods may be usedto predict the forces exerted on the bit as a result of bit/cuttingelement interaction with the bottomhole surface. For example, in onecase, a method for interpolating between calculated values of constraintforces may be used to predict the constraint forces on the drilling toolassembly or a different method of predicting the value of the constraintforces resulting from impact or frictional contact may be used. Further,a modified version of the method described above for predicting forcesresulting from cutting element interaction with the bottomhole surfacemay be used. These methods may be analytical, numerical (such as finiteelement method), or experimental. Alternatively, methods such asdisclosed in SPE Paper No. 29922 noted above or PCT Patent ApplicationNos. WO 00/12859 and WO 00/12860 may be used to model roller cone drillbit interaction with the bottomhole surface, or methods such asdisclosed in SPE papers no. 15617 and no. 15618 noted above may be usedto model fixed-cutter bit interaction with the bottomhole surface if afixed-cutter bit is used.

One of ordinary skill in the art will appreciate that the abovedescribed method of identifying design parameters for a drilling toolassembly may provide experience data useful in the training of ANNs.However, the above described method is merely exemplary, and is notintended as a limitation on the type of program that may provideexperience data. Thus, in certain embodiments, multiple drilling toolassembly design methods may be combined to provide a plurality ofsources of experience data, while in other embodiments, experience datamay include a single source of drilling tool assembly design data.Methods of simulating well drilling tool assemblies are discussed ingreater detail in U.S. Pat. Nos. 6,785,641 and 7,020,597 to Sujan Huang,assigned to the assignee of the present invention, and herebyincorporated by reference herein.

Method of Generating a Drilling Tool Assembly Order

Referring to FIG. 10, a flowchart diagram of a method of generating adrilling assembly order according to embodiments of the presentdisclosure is shown. In this embodiment, the method may include an ANNdriven optimization system, wherein required components for a drillingassembly are input 300 into the optimization system. Examples ofrequired components may include, for example, well data and/or otherdata a drilling engineer may optionally include as constraints in thedesign process. Additionally, remotely stored data, including experiencedata, historical bit run data, offset well data, and previous simulationdata, may then be input 302 into the optimization system. The data isthen analyzed 301 with an ANN to develop a drilling tool assembly. Thisanalyzing 301 may include, comparing experience data against any of theexperience data or training the ANN with new data, as described above.After the ANN 301 develops an initial drilling tool assembly, theoptimization system simulates 303 the initial drilling tool assembly inthe optimization system. In the system, the ANN driven optimizationsystem creates results data 304, and using such data, determines whetherto continue simulating the drilling tool assembly or terminate theoptimization.

In one embodiment, the optimization system may create results data 304and output the data to a visual display, as described above, in the formof a visual or graphical representation for a drilling engineer to view.However, in alternate embodiments, the system may use the results datato determine whether additional simulations are required to optimize thedrilling tool assembly. If the optimization system determines thatadditional optimization loops 305 are required, the system mayre-simulate the drilling tool assembly after adjusting the drilling toolassembly design according to suggestions for optimization included inthe results data. This loop 305 may continue until a specified conditionof the drilling tool assembly (e.g., a vibrational signature, a dullgrade, a wear condition, an ROP, or other conditions, as describedabove) is satisfied. When such a condition is satisfied, and thedrilling tool assembly is optimized, the optimized drilling toolassembly design is output, or otherwise processed by the optimizationsystem, to generate a drilling tool assembly order 306.

The drilling tool assembly order may include, for example, an orderguide for a number of drilling tool assembly components. Such componentsmay include drill collars, drill bits, cuttings elements, stabilizers,reamers, hole openers, tubular connections, or other components ofdrilling tool assembly that may be found on a BHA, a drill string, adrill bit, a sub, a stabilizer, a heavy weight drill plate, a downholemeasurement device, a downhole drive device, or other devicesincorporated into a drilling operation. Furthermore, in certain drillingoperations, the drilling tool assembly parameters, drilling parameters,conditions of the formation, and other drilling conditions may allow theoptimization system to determine and generate orders for other parts ofthe drilling operations. The order may be generated according to theanticipated requirements of a specific drilling operation. For example,in one embodiment, the results data and order generated may indicatethat an optimized drilling tool assembly includes a specific PDC drillbit with a hole opener, a spacer, and a particular drill collar, and anumber of rig instrumentation devices. The optimization system may thengenerate such an order, and communicate (i.e., output) the order to atleast one of a drilling engineer, an order service, a remote data store,or directly to the parts supplier. Thus, individual order components maybe included to each of the suppliers, or in some aspects, the entireorder may be generated such that contractor services may bid onsupplying the order. Furthermore, the optimization system may generateand store several orders, such that a single order may be communicatedto a supplier or a drilling engineer at a later time.

Examples of drilling component suppliers may include, but are notlimited to, any one of the following: a drilling contractor (providing adrilling rig and related tubular equipment (drill pipe, etc.)); riginstrumentation (responsible for process measurements related to welldrilling and construction); a drilling fluids contractor (responsiblefor drilling fluid used in drilling and completions phases of aproject); a directional drilling service (specialty personnel fordrilling directional well paths); a logging while drilling (LWD) ormeasurement while drilling (MWD) provider (a provider of tools used downhole to measure aspects of a well path); a mud logging service(geological and engineering data recording, analysis and presentation);pore pressure detection (a specialty service for maintaining safety inover-pressured drilling environments); a safety monitoring service(where poisonous gas is a possibility); a casing service (a specialtyservice for running casings into the well bore); a cementing service (aspecialty service for cementing steel casing in place in a well bore); acommunications or satellite provider (a communications service for dataand telephony from a rig site location); an equipment supplier (fuel,drilling water, potable water, food and housing services, and consumableitems such as drill bits, casing, materials, etc.); and transportationsuch as trucking, cranes, aircraft, support vessels for offshore wells).

The drilling optimization system may, in certain embodiments,communicate directly with the drilling component suppliers. In such anembodiment, a communication system, as described below, may provide amethod of communication between the optimization system and thesupplier.

Method of Communicating between the Optimization System and a Supplier

Referring now to FIG. 11, a schematic of drilling communications system500 in accordance with embodiments of the present disclosure is shown. Adrilling tool optimization system 502 is connected to a remote datastore 501. As data is collected from optimization system 502, the datais transmitted to data store 501.

In certain embodiment, the remote data store may use a WellsiteInformation Transfer Standard (“WITSML”) data transfer standard. Othertransfer standards may also be used without departing from the scope ofthe present disclosure.

Additional party connections to data store 501 may include an oilfieldservices supplier 503, an additional drilling simulator 504, and thirdparty and remote users 505. In some embodiments, each of the differentparties 502, 503, 504, 505 that have access to data store 501 may be indifferent locations. In addition, embodiments of the present disclosuredo not preclude supplier 503 from transmitting the LWD/MWD measurementdata to a separate site for analysis before the data is uploaded to datastore 501.

In addition to having data store 501 located on a secure server, in someembodiments, each of the parties connected to data store 501 has accessto view and update only specific portions of the data therein. Forexample, a supplier 503 may be restricted such that they cannot uploaddata related to drill cutting analysis, a measurement which is typicallynot performed by the vendor.

As measurement data becomes available, it may be uploaded to data store501. The data may be correlated to the particular position drilling toolassembly design parameter to which the data relates, a particular timestamp when the measurement was taken, or both. The simulated result data(e.g., WOB, TOB, RPM, vibrational signature etc.) will generally relateto a component of the drilling tool assembly as it is being simulated.As this data is uploaded to data store 501, it will typically becorrelated to a level of optimization when such result data wasobtained.

Thus, those of ordinary skill in the art will appreciate that remotecommunication and storage systems may allow direct communication betweena drilling optimization system and a supplier, or may otherwise allowthe dissemination of a drilling tool assembly order to a supplier. Whilethe above described system may provide such communication, other methodsof communication may also be utilized that include paper basedcommunication, direct networked communication, or displaying orderinformation to a drilling engineer. Additional description ofcommunication systems that may be used with embodiments disclosed hereinare described in U.S. Patent Application No. 60/765,694 to Moran, herebyincorporated by reference in its entirety.

Referring back to FIG. 10, in certain embodiments, the optimizationsystem may be able to generate surface equipment orders to order, forexample, shaker screens, drilling fluids, environmental units, cleaningoperations, drilling fluids, shakers, thermal desorption units,centrifuges, hydrocyclones, and/or other components used in drillingoperations. The specific components that may be required for a drillingoperation may be determined based on well data and experience data thatincludes, for example, the type of environmental cuttings remediationoperations that may be most effective for a certain formation. Forexample, in one embodiment, an output of the ANN, optimization system,or a results data may indicate that for a well drilling operation usingan specified optimized drilling tool assembly wellbore would operatemost efficiently using specified drilling parameters. Then, theoptimization system would compare the optimized drilling tool assemblyand the optimized drilling parameters to generate an order for aspecific drilling fluid that would further promote the optimizeddrilling parameters and the optimized drilling tool assembly. Theoptimization system then determines the about of formation (i.e., acuttings volume) being removed by such a drilling tool assemblyoperating in the formation (data which is already known by the system asa result of the simulation), and determines an appropriate environmentalremediation operation to handle the predicted volume of formationcuttings. Furthermore, the optimization system may include adetermination of which drilling fluids may be used in the operation.

For example, because the optimization system already has determined avolume of formation being removed, and an optimal ROP of the drillingoperation, the optimization system may further predict the number ofvibratory separators required to handle the cuttings volume.Additionally, because the optimization system has access to the drillingfluid types available for the drilling operation, the system may furtherselect any additional remediation tools that may be required to meet,for example, specified environmental regulations. Thus, in oneembodiment, the optimization system may generate an order for a drillingtool assembly, a volume of drilling fluid required for the operation,the number of vibratory shakers, the type of shaker screen that wouldmost effectively filter the formation (e.g., by based the filteringrequirements on a volume of cuttings, a fluid type, and a formationtype), and order any chemicals needed in other environmental remediationoperations. In addition to providing for an optimized drilling toolassembly design, the optimization system may be to used to generate anorder for the drilling tool assembly 306, as well as generate orders foradditional components that may be required in the drilling operation.

After the order is generated 306, the optimization system may output theorder in the form of generating paperwork 307 to be handled by adrilling engineer, or the order may be directly output to a supplier 308of the drilling tool assembly components, as described above. Thus,those of ordinary skill in the art will appreciate that the automatedordering capabilities of such a system may effectively remove the needfor human interaction with the optimization system during drilling toolassembly design. Furthermore, because the optimization system is drivenby an ANN, the system may be trained to, or may over time develop, anumber of neural interconnects that allow for optimized designs andordering procedures.

In another embodiment, after the drilling assembly order is generated306, the optimization system may analyze alternate components 309 todetermine if additional drilling tool assembly components are required.Additionally, in one aspect, after the optimization system determinesthe required components for a drilling operation, the system may enteranother optimization loop, as described above, to position the optimizedcomponents on the drill sting in an appropriate location. Thus, theoptimization system may, in certain embodiments, generate a drillingassembly order 306 for components, and while such order is beingprocessed, run additional optimization loops. The system may thengenerate a design guide for the drilling tool assembly and drillingoperations that specifies, for example, locations of components of thedrilling tool assembly, optimized drilling parameters for the drillingoperations, and additional components of the drilling operations. Insuch an embodiment, because the system is capable of generating detailedassembly orders including specific parts, tolerances, materials,locations, and operating parameters, the optimization system may be usedto develop a plan for a drilling operation. The plan that is generatedby the optimization system may include choices of one or more ANNs, andmay thus be generated in less time, and with greater optimizationpotential than previously attainable.

Advantageously, embodiments of the present disclosure provide drillingengineers with an optimization system that is ANN driven, and may thusreduce the number of iterative simulations required when designingdrilling tool assemblies. Additionally, by reducing the number ofsimulations required to optimize drilling tool assemblies, the workprocess for designing such drilling tool assemblies may be moreefficient, thereby decreasing the cost of the design of such drillingtool assemblies. Furthermore, the optimization system may be dynamicallyupgradeable, such that each iterative simulation of a drilling toolassembly is stored in a database, and may be used to train ANNs tofurther increase the robustness of the optimization system. Suchsimulation data, and the analyzed results of the ANN may also be used toincrease the speed of subsequent simulations. Thus, the more times theANN simulates a drilling tool assembly, the more efficient theoptimization is during the design on subsequent initial drilling toolassemblies.

Because each initial drilling tool assembly design is based, at least inpart, on all preceding optimizations, as the neural interconnects of theANN increase, the efficiency of the system will also increase. Also,advantageously, embodiments of the present disclosure may be used todevelop drilling tool assembly designs with specified desirableconditions. Examples of such conditions may include, drilling toolassemblies with an optimal vibration signature, dull grade, bit wear, orother specific optimized parameter.

In certain embodiments of the present disclosure, methods disclosedherein may provide a drilling engineer with a substantially automatedsystem for designing drilling tool assemblies, and subsequently, orderssuch assemblies. As described above, methods disclosed herein providefor an optimization system that may generate and order drilling toolassemblies, drilling operation components, and determine the placementof such components of a drilling operation. Thus, the optimizationsystem may further increase the efficiency of the entire drillingoperation by selecting components of a drilling operation withoutadditional input from a drilling engineer.

While the present disclosure has been described with respect to alimited number of embodiments, those skilled in the art, having benefitof this disclosure, will appreciate that other embodiments may bedevised which do not depart from the scope of the disclosure asdescribed herein. Accordingly, the scope of the present disclosureshould be limited only by the attached claims.

1-23. (canceled)
 24. A method of optimizing a drilling tool assemblycomprising: inputting well data into an optimization system, theoptimization system including an experience data set and an artificialneural network; comparing the well data to the experience data set;developing an initial drilling tool assembly based on the comparing thewell data to the experience data, the initial drilling tool assemblyincluding at least one expandable tool, wherein the drilling toolassembly is developed using the artificial neural network; simulatingthe initial drilling tool assembly in the optimization system; creatingresult data in the optimization system based on the simulating; andoptimizing placement of the expandable tool in the drilling toolassembly based on the result data.
 25. The method of claim 24, whereinthe optimizing placement of the at least one expandable tool in thedrilling tool assembly comprises: adjusting at least one drilling toolassembly design parameter of the initial drilling tool assembly based onthe result data to produce an adjusted drilling tool assembly;re-simulating the adjusted drilling tool assembly in the optimizationsystem to produce a second result data; and adjusting at least onedrilling tool assembly design parameter of the adjusted drilling toolassembly based on the second result data.
 26. The method of claim 25,further comprising: continuing the optimizing until an optimizedplacement of the at least one expandable tool is achieved.
 27. Themethod of claim 24, wherein the experience data comprises at least oneselected from a group consisting of historical bit run data, offset welldata, and prior simulation data.
 28. The method of claim 24, wherein thedrilling tool assembly design parameter is at least one selected from agroup consisting of a bit, a cutting element, a drilling tool assemblycomponent, a mud weight, a weight on bit, a rotary torque, a rotaryspeed, a lateral force on bit, a ratio of forces on cones, a ratio offorces between the cones, a distribution of forces on cutting elements,a volume of formation cut, and a wear on cutting elements.
 29. Themethod of claim 24, wherein the simulating the initial drilling toolassembly in the optimization system comprises simulating the at leastone expandable tool in at least one of an expanded position and acollapsed position.
 30. The method of claim 24, further comprising:generating a drilling tool assembly order based on the result data; andoutputting the drilling tool assembly order.
 31. The method of claim 24,wherein the at least one expandable tool comprises at least one of agroup consisting of an expandable reamer and an expandable stabilizer.32. The method of claim 24, wherein the experience data set is stored ina remote web store.
 33. The method of claim 32, wherein the remote webstore is accessible by the artificial neural network.
 34. The method ofclaim 24, wherein the well data comprises at least one of a groupconsisting of a well type, a formation type, an acceptable vibrationalsignature, an acceptable rate of penetration, an acceptable wear rate,and an acceptable well path direction.
 35. The method of claim 24,further comprising: training the artificial neural network with theexperience data set before developing the initial drilling toolassembly.
 36. The method of claim 35, wherein the trained neural networkcomprises at least one of a vibrational artificial neural network, a bitwear artificial neural network, a rate of penetration artificial neuralnetwork, a directional artificial neural network, and a mud flow rateartificial neural network.
 37. A method of designing a drilling toolassembly comprising: inputting well data into an optimization system,the optimization system including an experience data set and anartificial neural network; comparing the well data to the experiencedata set; developing an initial drilling tool assembly based on thecomparing the well data to the experience data, the initial drillingtool assembly including at least one expandable tool, wherein theinitial drilling tool assembly is developed using the artificial neuralnetwork; simulating the drilling tool assembly in the optimizationsystem, the simulating comprising simulating the at least one expandabletool in at least one of an expanded position and a collapsed position;determining a vibrational signature of the initial drilling toolassembly; and adjusting a placement of the at least one expandable toolin the initial drilling tool assembly based on the vibrational signatureto produce an adjusted drilling tool assembly.
 38. The method of claim37, wherein the adjusting the initial drilling tool assembly based onthe vibrational signature comprises: adjusting the location of theexpandable tool on a drill string.
 39. The method of claim 37, furthercomprising: simulating the adjusted drilling tool assembly in theoptimization system; adjusting at least one drilling tool assemblydesign parameter based on the simulation of the adjusted drilling toolassembly; and terminating the simulating and the adjusting when thedrilling tool assembly is optimized.
 40. The method of claim 37, furthercomprising: training the artificial neural network in a drillingapplication to create a trained artificial neural network, wherein thetraining comprises: providing a training data set to the artificialneural network, wherein the data set comprises known input variables andknown output variables that correspond to the input variables; andproviding experience data to the artificial neural network, andpredicting vibrational responses for the drilling tool assembly with thetrained artificial neural network predicts.
 41. A method of optimizing adrilling tool assembly comprising: inputting well data into anoptimization system, the optimization system including an experiencedata set and an artificial neural network; comparing the well data tothe experience data set; developing an initial drilling tool assemblybased on the comparing the well data to the experience data, the initialdrilling tool assembly including at least one of a stabilizer and areamer, wherein the drilling tool assembly is developed using theartificial neural network; simulating the initial drilling tool assemblyin the optimization system; creating result data in the optimizationsystem based on the simulating; and optimizing placement of the at leastone stabilizer and reamer in the drilling tool assembly based on theresult data.
 42. The method of claim 41, further comprising: continuingthe optimizing until an optimized placement of the at least onestabilizer and reamer is achieved.
 43. The method of claim 41, furthercomprising: wherein the simulating the initial drilling tool assembly inthe optimization system comprises simulating the at least one stabilizerand reamer in at least one of an expanded position and a collapsedposition.